In some countries, businesses may require making unofficial payments or gifts to "get things done." The indicators below capture the prevalence of different types of bribery in 139 countries. The results are based on surveys of more than 131,000 firms. A database query tool is available to help you better understand the prevalence of corruption across various firm subgroups. You can also generate graphs to compare countries.

To see the details for a specific economy, click on the links below. Click on column headers to sort data.



Custom Data Set

Generate a Custom Data Set for Corruption including standard errors, indicator values by firm subgroups, historical data and selected countries.


Bribery incidence (percent of firms experiencing at least one bribe payment request)Percent of firms experiencing at least one bribe payment request during 6 transactions dealing with utilities access, permits, licences, and taxes.
Bribery depth (% of public transactions where a gift or informal payment was requested)Bribery depth is the percentage of transactions (out of 6 transactions dealing with utilities access, permits, licences, and taxes) where a gift or informal payment was requested.
Percent of firms expected to give gifts in meetings with tax officialsPercent of firms expected to give gifts or an informal payment in meetings with tax officials.
Percent of firms expected to give gifts to secure government contractPercent of establishments that consider that firms with characteristics similar to theirs are making informal payments or giving gifts to public officials to secure government contract.
Value of gift expected to secure a government contract (% of contract value)Percentage of the contract value expected as a gift to secure a government contract. Only firms that have confirmed that they have secured or attempted to secure a government contract in the last 12 months were required to answer this question.
Percent of firms expected to give gifts to get an operating licensePercent of firms expected to give gifts or an informal payment to get an operating license.
All Countries
East Asia & Pacific29.423.320.345.62.421.7
Europe & Central Asia17.313.513.124.61.514.1
High income nonOECD7.
High income: OECD1.91.30.714.31.83.3
Latin America & Caribbean9.56.76.819.81.07.1
Middle East & North Africa23.820.519.340.30.915.9
South Asia24.821.019.645.52.925.3
Sub-Saharan Africa24.018.318.035.62.718.0
Afghanistan (2014)46.834.634.046.94.431.6
Albania (2013)19.516.718.434.30.311.8
Angola (2010)51.342.934.264.09.839.0
Antigua and Barbuda (2010)
Argentina (2010)
Armenia (2013)
Azerbaijan (2013)15.913.812.543.7035.2
Bahamas, The (2010)21.014.810.97.8017.8
Bangladesh (2013)47.743.941.048.92.958.0
Barbados (2010)
Belarus (2013)
Belize (2010)
Benin (2016)14.512.411.041.84.6n.a.
Bhutan (2015)
Bolivia (2017)
Bosnia and Herzegovina (2013)
Botswana (2010)
Brazil (2009)
Bulgaria (2013)
Burkina Faso (2009)
Burundi (2014)30.319.920.
Cabo Verde (2009)
Cambodia (2016)64.759.458.787.50.950.3
Cameroon (2016)26.722.620.451.92.847.6
Central African Republic (2011)21.014.616.840.84.35.8
Chad (2009)35.027.621.247.34.352.6
Chile (2010)
China (2012)11.69.910.942.20.27.8
Colombia (2010)
Congo, Dem. Rep. (2013)56.551.053.951.94.047.1
Congo, Rep. (2009)37.530.937.175.23.7n.a.
Costa Rica (2010)
Côte d'Ivoire (2016)25.319.522.935.72.010.8
Croatia (2013)3.92.5014.01.11.3
Czech Republic (2013)
Djibouti (2013)
Dominica (2010)
Dominican Republic (2016)12.311.011.923.20.318.6
Ecuador (2017)
Egypt, Arab Rep. (2016)15.213.613.214.2020.7
El Salvador (2016)
Eritrea (2009)000000
Estonia (2013)000000
Ethiopia (2015)26.819.817.419.70.52.3
Fiji (2009)
Gabon (2009)
Gambia, The (2006)
Georgia (2013)
Ghana (2013)18.714.711.435.22.219.1
Grenada (2010)
Guatemala (2010)
Guinea (2016)
Guinea-Bissau (2006)27.619.222.748.92.815.3
Guyana, CR (2010)
Honduras (2016)
Hungary (2013)2.11.1051.814.110.1
India (2014)22.719.615.339.80.125.8
Indonesia (2015)30.627.121.633.02.919.3
Iraq (2011)37.333.829.268.71.824.2
Israel (2013)0.1001.100
Jamaica (2010)19.317.914.321.1019.0
Jordan (2013)12.710.414.414.102.6
Kazakhstan (2013)26.722.022.319.10.815.8
Kenya (2013)26.416.717.433.42.315.6
Kosovo (2013)
Kyrgyz Republic (2013)59.853.654.855.12.459.6
Lao PDR (2016)16.414.613.774.308.6
Latvia (2013)
Lebanon (2013)19.214.314.630.20.211.8
Lesotho (2016)14.69.815.731.72.52.2
Liberia (2017)56.141.541.742.82.944.8
Lithuania (2013)10.49.810.912.90.36.6
Macedonia, FYR (2013)
Madagascar (2013)32.932.031.744.71.645.7
Malawi (2014)
Malaysia (2015)28.221.923.751.43.328.8
Mali (2016)33.727.
Mauritania (2014)28.922.220.436.62.05.9
Mauritius (2009)
Mexico (2010)17.69.610.434.94.517.5
Micronesia, Fed. Sts. (2009)4.51.00000
Moldova (2013)
Mongolia (2013)33.425.819.525.91.529.7
Montenegro (2013)18.812.316.2000
Morocco (2013)37.229.528.758.40.216.5
Mozambique (2007)
Myanmar (2016)29.326.720.
Namibia (2014)
Nepal (2013)14.410.910.464.54.418.9
Nicaragua (2016)
Niger (2017)
Nigeria (2014)28.926.025.928.61.624.2
Pakistan (2013)30.828.528.888.28.231.0
Panama (2010)
Papua New Guinea (2015)26.419.3023.10.918.7
Paraguay (2017)13.89.316.
Peru (2010)
Philippines (2015)17.212.414.120.50.310.0
Poland (2013)
Romania (2013)
Russian Federation (2012)
Rwanda (2011)
Samoa (2009)30.522.419.618.91.915.4
Senegal (2014)
Serbia (2013)
Sierra Leone (2017)46.131.429.671.016.827.6
Slovak Republic (2013)
Slovenia (2013)
Solomon Islands (2015)43.827.430.945.42.328.0
South Africa (2007)
South Sudan (2014)48.033.830.634.44.735.9
Sri Lanka (2011)
St. Kitts and Nevis (2010)2.21.6016.50.41.2
St. Lucia (2010)11.610.911.92.705.8
St. Vincent and the Grenadines (2010)
Sudan (2014)
Suriname (2010)
Swaziland (2016)
Sweden (2014)
Tajikistan (2013)36.329.631.933.62.028.7
Tanzania (2013)20.815.714.666.23.617.0
Thailand (2016)
Timor-Leste (2015)44.227.517.181.414.522.9
Togo (2016)
Tonga (2009)24.913.51.422.305.0
Trinidad and Tobago (2010)
Tunisia (2013)
Turkey (2013)
Uganda (2013)22.014.614.320.20.817.6
Ukraine (2013)50.444.750.
Uruguay (2010)
Uzbekistan (2013)
Vanuatu (2009)
Venezuela, R.B. (2010)
Vietnam (2015)26.121.725.056.92.814.5
West Bank and Gaza (2013)
Yemen, Rep. (2013)64.360.962.683.84.861.6
Zambia (2013)
Zimbabwe (2016)17.512.312.621.10.29.0
  • Notes

    * This indicator is computed using data from manufacturing firms only.

    Additional Notes

    1. Most surveys were administered using the Enterprise Surveys Global Methodology as outlined in the Methodology page, while some others did not strictly adhere to the Enterprise Surveys Global Methodology. For example, for surveys which do not follow the Global Methodology, the Universe under consideration may have consisted of only manufacturing firms or the questionnaire used may have been different from the standard global questionnaire. Data users should exercise caution when comparing raw data and point estimates between surveys that did and did not adhere to the Enterprise Surveys Global Methodology. For surveys which did not adhere to the Global Methodology plus Afghanistan 2008, any inference from one of these surveys is representative only for the data sample itself.
    2. Regional and "all countries" averages of indicators are computed by taking a simple average of country-level point estimates. For each economy, only the latest available year of survey data is used in this computation. Only surveys, posted during the years 2010-2017, and adhering to the Enterprise Surveys Global Methodology are used to compute these regional and "all countries" averages.
    3. Descriptions of firm subgroup levels, e.g. how the ex post groupings are constructed, are provided in the Indicator Descriptions (PDF, 710KB) document.
    4. Statistics derived from less than or equal to five firms are displayed with an "n.a." to maintain confidentiality and should be distinguished from ".." which indicates missing values. Also note for three growth-related indicators under the "Performance" topic, these indicators are not computed when they are derived from less than 30 firms.
    5. Standard errors are labeled "n.c.", meaning not computed, for the following:

           1) indicators for all surveys that were not conducted using the Enterprise Surveys Global Methodology and

           2) for indicator breakdowns by ex post groupings: exporter or ownership type, and gender of the top manager.
    6. Please cite our data as follows:

      Enterprise Surveys (, The World Bank.